Technology

Physical AI: software for the real world

Physical AI is AI that works on the physical world rather than only on text and screens — sensing what is happening, modeling it, and acting on it. Toobler builds the software foundation that makes it possible: the connected assets, the operational twin, the models, and the governed layer that acts.

Discuss your assets See the stack in practice

The stack

Sense, model, learn, act

  1. Sense — connected assets

    Industrial IoT: sensors, gateways and the cloud layer that brings physical assets online.

  2. Model — operational digital twin

    A live representation of the assets and processes, built on your real data.

  3. Learn — industrial AI

    Prediction, anomaly detection and decision support on operational data.

  4. Act — governed agents

    Action taken through a governed, human-approved and audited agent layer.

The extruder digital twin on a laptop at the line: live status, temperature recommendations with the reason for each, and a 3D view of the machine with live data points.

Proof

Sensing, modeling and acting on a live production line

A real-time physics digital twin of an extrusion line, and an assistant that recommends the smallest dial change that clears a fault

How Toobler built a real-time physics digital twin and an AI recommender for a plastics extrusion line, advising the smallest dial change that clears a fault.

Read the case study

Proof

Where it has run before

Bringing AI to a physical operation?

Map your assets, your data and the path from sensing to acting.

Discuss your assets

Related

Frequently asked

What is physical AI?
AI that senses, understands and acts on the physical world — as opposed to purely digital AI. In practice it is a stack: connected assets, a model of the operation, AI on that data, and a governed way to act.
Do you build robotics or autonomous machines?
No. We build the software foundation — connectivity, operational models, AI on operational data and the governed action layer — not robotics or embodied autonomy.
Where does it start?
Almost always with connecting the assets and data. The intelligence is only as good as the operational picture underneath it.

Start with one measurable use case.

A Readiness Sprint is a fixed-scope engagement that maps your integration and AI readiness and produces a production-oriented plan — before anything is built.

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